Characterization of Kaposi\u27s Sarcoma-Associated Herpesvirus ORF11 as a Possible dUTPase

A dUTPase is a crucial enzyme that hydrolyzes dUTP to dUMP. This reaction prevents the mutagenic or lethal misincorporation of uracil into DNA. For that reason, the enzyme is required for efficient DNA replication. Previous studies have shown that has ORFl l dUTPase-like motifs and thus may be a dUTPase. Generally, gammaherpesviruses contain six characteristic dUTPase motifs. In particular ORFl 1 and contains motifs 1, 2, 4, 6. While the characteristic motifs of gamroaberpesviruses include motifs 1, 2, 3, 4, 5, and 6, the number of dUTPase-like motifs in ORFl 1 \u27s protein sequence is substantial. Thus, ORFl 1 may be a dUTPase. To further investigate this hypothesis, ORFl 1 was cloned, expressed in E. coli, and the protein was examined in a dUTPase assay. ORFl 1 was cloned into pGEX-5x-3 through a sequential process starting with a polymerase chain reaction (PCR) and ending with the isolation of plasmid DNA containing ORFl 1. The positive clones were sequenced and confirmed. Subsequently, ORFl 1 was expressed as a GST fusion protein in E. coli. Verification of expression was done by purifying the GST proteins using glutathione beads. The purified proteins were evaluated by SOS-PAGE. The SOS-PAGE demonstrated purification of proteins with their expected sizes. Finally, the lack of dUTPase activity was demonstrated by a dUTPase assay. Bacterial extracts expressing ORFl 1 were incubated with dUTP and the separation of nucleotides was evaluated using thin layer chromatography. In summary, our results demonstrate that Kaposi\u27s sarcoma-associated herpesvirus ORFl 1 does not code for a functional dUTPase. Further studies are needed to determine the function of the protein

Characterization of Kaposi\u27s Sarcoma-Associated Herpesvirus ORF11 as a Possible dUTPase

A dUTPase is a crucial enzyme that hydrolyzes dUTP to dUMP. This reaction prevents the mutagenic or lethal misincorporation of uracil into DNA. For that reason, the enzyme is required for efficient DNA replication. Previous studies have shown that has ORFl l dUTPase-like motifs and thus may be a dUTPase. Generally, gammaherpesviruses contain six characteristic dUTPase motifs. In particular ORFl 1 and contains motifs 1, 2, 4, 6. While the characteristic motifs of gamroaberpesviruses include motifs 1, 2, 3, 4, 5, and 6, the number of dUTPase-like motifs in ORFl 1 \u27s protein sequence is substantial. Thus, ORFl 1 may be a dUTPase. To further investigate this hypothesis, ORFl 1 was cloned, expressed in E. coli, and the protein was examined in a dUTPase assay. ORFl 1 was cloned into pGEX-5x-3 through a sequential process starting with a polymerase chain reaction (PCR) and ending with the isolation of plasmid DNA containing ORFl 1. The positive clones were sequenced and confirmed. Subsequently, ORFl 1 was expressed as a GST fusion protein in E. coli. Verification of expression was done by purifying the GST proteins using glutathione beads. The purified proteins were evaluated by SOS-PAGE. The SOS-PAGE demonstrated purification of proteins with their expected sizes. Finally, the lack of dUTPase activity was demonstrated by a dUTPase assay. Bacterial extracts expressing ORFl 1 were incubated with dUTP and the separation of nucleotides was evaluated using thin layer chromatography. In summary, our results demonstrate that Kaposi\u27s sarcoma-associated herpesvirus ORFl 1 does not code for a functional dUTPase. Further studies are needed to determine the function of the protein

Non-mandated reporters\u27 attitudes on reporting child abuse

The purpose of this study is to identify what barriers to reporting child abuse may exist within the population of non-mandated reporters. The number of child abuse reports being filed is on the rise and many of these reports are being evaluated out (not needing follow up)

Prostaglandin E2 promotes features of replicative senescence in chronically activated human CD8+ T cells.

Prostaglandin E2 (PGE2), a pleiotropic immunomodulatory molecule, and its free radical catalyzed isoform, iso-PGE2, are frequently elevated in the context of cancer and chronic infection. Previous studies have documented the effects of PGE2 on the various CD4+ T cell functions, but little is known about its impact on cytotoxic CD8+ T lymphocytes, the immune cells responsible for eliminating virally infected and tumor cells. Here we provide the first demonstration of the dramatic effects of PGE2 on the progression of human CD8+ T cells toward replicative senescence, a terminal dysfunctional state associated multiple pathologies during aging and chronic HIV-1 infection. Our data show that exposure of chronically activated CD8+ T cells to physiological levels of PGE2 and iso-PGE2 promotes accelerated acquisition of markers of senescence, including loss of CD28 expression, increased expression of p16 cell cycle inhibitor, reduced telomerase activity, telomere shortening and diminished production of key cytotoxic and survival cytokines. Moreover, the CD8+ T cells also produced higher levels of reactive oxygen species, suggesting that the resultant oxidative stress may have further enhanced telomere loss. Interestingly, we observed that even chronic activation per se resulted in increased CD8+ T cell production of PGE2, mediated by higher COX-2 activity, thus inducing a negative feedback loop that further inhibits effector function. Collectively, our data suggest that the elevated levels of PGE2 and iso-PGE2, seen in various cancers and HIV-1 infection, may accelerate progression of CD8+ T cells towards replicative senescence in vivo. Inhibition of COX-2 activity may, therefore, provide a strategy to counteract this effect

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys

Applications of Statistical Experimental Designs to Improve Statistical Inference in Weed Management

In a balanced design, researchers allocate the same number of units across all treatment groups. It has been believed as a rule of thumb among some researchers in agriculture. Sometimes, an unbalanced design outperforms a balanced design. Given a specific parameter of interest, researchers can design an experiment by unevenly distributing experimental units to increase statistical information about the parameter of interest. An additional way of improving an experiment is an adaptive design (e.g., spending the total sample size in multiple steps). It is helpful to have some knowledge about the parameter of interest to design an experiment. In the initial phase of an experiment, a researcher may spend a portion of the total sample size to learn about the parameter of interest. In the later phase, the remaining portion of the sample size can be distributed in order to gain more information about the parameter of interest. Though such ideas have existed in statistical literature, they have not been applied broadly in agricultural studies. In this article, we used simulations to demonstrate the superiority of the experimental designs over the balanced designs under three practical situations: comparing two groups, studying a dose-response relationship with right-censored data, and studying a synergetic effect of two treatments. The simulations showed that an objective-specific design provides smaller error in parameter estimation and higher statistical power in hypothesis testing when compared to a balanced design. We also conducted an adaptive experimental design applied to a dose-response study with right-censored data to quantify the effect of ethanol on weed control. Retrospective simulations supported the benefit of this adaptive design as well. All researchers face different practical situations, and appropriate experimental designs will help utilize available resources efficiently

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys

Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data

In this paper we introduce fuzzy forests, a novel machine learning algorithm for ranking the importance of features in high-dimensional classification and regression problems. Fuzzy forests is specifically designed to provide relatively unbiased rankings of variable importance in the presence of highly correlated features, especially when the number of features, p, is much larger than the sample size, n (p n). We introduce our implementation of fuzzy forests in the R package, fuzzyforest. Fuzzy forests works by taking advantage of the network structure between features. First, the features are partitioned into separate modules such that the correlation within modules is high and the correlation between modules is low. The package fuzzyforest allows for easy use of the package WGCNA (weighted gene coexpression network analysis, alternatively known as weighted correlation network analysis) to form modules of features such that the modules are roughly uncorrelated. Then recursive feature elimination random forests (RFE-RFs) are used on each module, separately. From the surviving features, a final group is selected and ranked using one last round of RFE-RFs. This procedure results in a ranked variable importance list whose size is pre-specified by the user. The selected features can then be used to construct a predictive model

Who Voted in 2016? Using Fuzzy Forests to Understand Voter Turnout

Objective: What can machine learning tell us about who voted in 2016? There are numerous competing voter turnout theories, and a large number of covariates are required to assess which theory best explains turnout. This article is a proof of concept that machine learning can help overcome this curse of dimensionality and reveal important insights in studies of political phenomena. Methods: We use fuzzy forests, an extension of random forests, to screen variables for a parsimonious but accurate prediction. Fuzzy forests achieve accurate variable importance measures in the face of high‐dimensional and highly correlated data. The data that we use are from the 2016 Cooperative Congressional Election Study. Results: Fuzzy forests chose only a small number of covariates as major correlates of 2016 turnout and still boasted high predictive performance. Conclusion: Our analysis provides three important conclusions about turnout in 2016: registration and voting procedures were important, political issues were important (especially Obamacare, climate change, and fiscal policy), but few demographic variables other than age were strongly associated with turnout. We conclude that fuzzy forests is an important methodology for studying overdetermined questions in social sciences